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Minimum Data Set Changes in Health, End-Stage Disease and Symptoms and Signs Scale: A Revised Measure to Predict Mortality in Nursing Home Residents.

Ogarek JA, McCreedy EM, Thomas KS, Teno JM, Gozalo PL. Minimum Data Set Changes in Health, End-Stage Disease and Symptoms and Signs Scale: A Revised Measure to Predict Mortality in Nursing Home Residents. Journal of the American Geriatrics Society. 2018 May 1; 66(5):976-981.

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Abstract:

OBJECTIVES: To revise the Minimum Data Set (MDS) Changes in Health, End-stage disease and Symptoms and Signs (CHESS) scale, an MDS 2.0-based measure widely used to predict mortality in institutional settings, in response to the release of MDS 3.0. DESIGN: Development of a predictive scale using observational data from the MDS and Medicare Master Beneficiary Summary File. SETTING: All Centers for Medicare and Medicaid Services (CMS)-certified nursing homes in the United States. PARTICIPANTS: Development cohort of 1.3 million Medicare beneficiaries newly admitted to a CMS-certified nursing home during 2012. Primary validation cohort of 1.2 million Medicare recipients who were newly admitted to a CMS-certified nursing home during 2013. MEASUREMENTS: Items from the MDS 3.0 assessments identified as likely to predict mortality. Death information was obtained from the Medicare Master Beneficiary Summary File. RESULTS: MDS-CHESS 3.0 scores ranges from 0 (most stable) to 5 (least stable). Ninety-two percent of the primary validation sample with a CHESS scale score of 5 and 15% with a CHESS scale of 0 died within 1 year. The risk of dying was 1.63 times as great (95% CI = 1.628-1.638) for each unit increase in CHESS scale score. The MDS-CHESS 3.0 is also strongly related to hospitalization within 30 days and successful discharge to the community. The scale predicted death in long-stay residents at 30 days (C = 0.759, 95% confidence interval (CI) = 0.756-0.761), 60 days (C = 0.716, 95% CI = 0.714-0.718) and 1 year (C = 0.655, 95% CI = 0.654-0.657). CONCLUSION: The MDS-CHESS 3.0 predicts mortality in newly admitted and long-stay nursing home populations. The additional relationship to hospitalizations and successful discharges to community increases the utility of this scale as a potential risk adjustment tool.





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